# App Exploration Phase

> Before deploying against an opaque app, run an exploration phase to build a per-element knowledge base the agent retrieves at task time.

- **Category**: Agentic AI
- **Subcategory**: Tool Use & Environment
- **Canonical URL**: https://designpattern.fyi/patterns/app_exploration_phase/

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## Description
**Intent**: Teach the agent what every button does before it has to act on any of them.

**Context**: You need an agent to drive a mobile or desktop app with no public API and no accessibility labels that name its controls. The only way to learn what a control does is to click it and see what happens — and you'll be running this agent many times.

**Solution**: Split the lifecycle into two phases. (1) **Exploration** — the agent pokes around autonomously, or watches a human demo, and writes per-element docs: what it is, what it does, when to use it. Stored in a structured knowledge base. (2) **Deployment** — for each task, retrieve the relevant element docs via vector search, inject into context, then act. Refresh docs when the UI changes.


## Use Cases
- The agent must drive a GUI app with no API docs for its UI elements.
- The agent will run against the same app many times, so upfront exploration cost amortizes.
- UI element semantics are stable enough to document once.





## Trade-offs


### Advantages

- Deployment-time actions are grounded in learned semantics, not guesses.

- One exploration run pays for itself across many user tasks.

- Human-demo mode makes onboarding a new app low-effort.




### Considerations & Drawbacks

- Exploration is expensive and offline — production tasks must wait or run on a stale KB.

- KB drift when the app updates; staleness is non-trivial to detect automatically.

- Deployment quality is ceiling'd by how good the exploration docs are.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/app-exploration-phase/)